A Phase IIB clinical trial typically is a single-arm study aimed at deciding whether a new treatment E is sufficiently promising, relative to a standard therapy, S, to include in a large-scale randomized trial. Thus, Phase IIB trials are inherently comparative even though a standard therapy arm usually is not included. Uncertainty regarding the response rate theta s of S is rarely made explicit, either in planning the trial or interpreting its results. We propose practical Bayesian guidelines for deciding whether E is promising relative to S in settings where patient response is binary and the data are monitored continuously. The design requires specification of an informative prior for theta s, a targeted improvement for E, and bounds on the allowed sample size. No explicit specification of a loss function is required. Sampling continues until E is shown to be either promising or not promising relative to S with high posterior probability, or the maximum sample size is reached. The design provides decision boundaries, a probability distribution for the sample size at termination, and operating characteristics under fixed response probabilities with E.